Please use this identifier to cite or link to this item:
http://hdl.handle.net/1843/47434| Type: | Artigo de Periódico |
| Title: | Managing supply chain resources with big data analytics: a systematic review |
| Authors: | Marcelo Werneck Barbosa Alberto de la Calle Vicente Marcelo Bronzo Ladeira Marcos Paulo de Oliveira Valadares |
| Abstract: | Big Data Analytics (BDA) has the potential to improve demand forecasting, communications and better manage supply chain resources. Despite such recognised benefits and the increase of BDA research, little is known about the general approaches used to investigate BDA in the context of supply chain management (SCM). In the light of the Resource-based View, the main goal of this study was, by means of a systematic literature review, to comprehend how BDA has been investigated on SCM studies, which resources are managed by BDA as well as which SCM processes are involved. Our study found out that the predictive and prescriptive approaches are more frequently used and organisational, technological and human resources are often managed by BDA. It was observed a focus on Demand Management and Order Fulfilment processes and a lack of studies on Returns Management, which indicates an open research area that should be exploited by future studies. |
| Subject: | Logística empresarial |
| language: | eng |
| metadata.dc.publisher.country: | Brasil |
| Publisher: | Universidade Federal de Minas Gerais |
| Publisher Initials: | UFMG |
| metadata.dc.publisher.department: | FCE - DEPARTAMENTO DE CIÊNCIAS ADMINISTRATIVAS |
| Rights: | Acesso Restrito |
| metadata.dc.identifier.doi: | 10.1080/13675567.2017.1369501 |
| URI: | http://hdl.handle.net/1843/47434 |
| Issue Date: | 2017 |
| metadata.dc.url.externa: | http://https://www.tandfonline.com/doi/full/10.1080/13675567.2017.1369501 |
| metadata.dc.relation.ispartof: | International Journal of Logistics Research and Applications |
| Appears in Collections: | Artigo de Periódico |
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.